Dynamic sensitivity analysis of a mathematical model describing the effect of the macroalgaeAsparagopsis taxiformison rumen fermentation and methane production underin vitrocontinuous conditions

Abstract

AbstractRuminants plays an important role in global warming by emitting enteric methane (CH4) through the degradation of feeds by the rumen microbiota. To better understand the dynamics fermentation outputs, including methane and volatile fatty acids (VFA) production, mathematical models have been developed. Sensitivity analysis (SA) methods quantify the contribution of model input parameters (IP) to the variation of an output variable of interest. In animal science, SA are usually conducted in static condition. In this work, we hypothesized that including the dynamic aspect of the rumen fermentation to SA can be useful to inform on optimal experimental conditions aimed at quantifying the key mechanisms driving CH4and VFA production. Accordingly, the objective of this work was to conduct a dynamic SA of a rumen fermentation model underin vitrocontinuous conditions (close to the realin vivoconditions). Our model case study integrates the effect of the macroalgaeAsparagopsis taxiformis(AT) on the fermentation. AT has been identified as a potent CH4inhibitor via the presence of bromoform, an anti-methanogenic compound. We implemented two SA methods. We computed Shapley effects and full and independent Sobol indices over time for quantifying the contribution of 16 IPs to CH4(mol/h) and VFA (mol/l) variation. Our approach allows to discriminate the 3 contribution types of an IP to output variable variation (individual, via the interactions and via the dependence/correlation). We studied three diet scenarios accounting for several doses of AT relative to Dry Matter (DM): control (0% DM of AT), low treatment (LT: 0.25% DM of AT) and high treatment (HT: 0.50% DM of AT). Shapley effects revealed that hydrogen (H2) utilizers microbial group via its Monod H2affinity constant highly contributed (> 50%) to CH4variation with a constant dynamic over time for control and LT. A shift on the impact of microbial pathways driving CH4variation was revealed for HT. IPs associated with the kinetic of bromoform utilization and with the factor modeling the direct effect of bromoform on methanogenesis were identified as influential on CH4variation in the middle of fermentation. Whereas, VFA variation for the 3 diet scenarios was mainly explained by the kinetic of fibers degradation, showing a high constant contribution (> 30%) over time. In addition, the Sobol indices indicated that interactions between IPs played a role on CH4variation, which was not the case of VFA variation. However, these results are dependent on the way interactions are represented in the model. The simulations computed for the SA were also used to analyze prediction uncertainty. It was related to the dynamic of dry matter intake (DMI, g/h), increasing during the high intake activity periods and decreasing when the intake activity was low. Moreover, CH4(mol/h) simulations showed a larger variability than VFA simulations, suggesting that the reduction of the uncertainty of IPs describing the activity of the H2utilizers microbial group is a promising lead to reduce the overall model uncertainty. Our results highlighted the dynamic nature of the influence of metabolic pathways on CH4productions under an anti-methanogenic treatment. SA tools can be further exploited to design optimal experiments studying rumen fermentation and CH4mitigation strategies. These optimal experiments would be useful to build robust models that can guide the development of sustainable nutrition strategies.